The invention relates generally to medical imaging, and more particularly to a technique for image segmentation using a model.
Medical imaging has become an extremely valuable tool in the diagnosis ad treatment of many illnesses and diseases. For example, cardiac imaging using computed tomography (CT) is emerging as the protocol of choice for the diagnosis and treatment of cardiovascular disease. In addition to standard X-ray systems that produce an image on a film, medical imaging systems are now available that produce digital images that may be displayed on a monitor.
Digital imaging processing enables medical images to be enhanced through the use of computers. Digital image processing has many of the same advantages in signal processing over analog image processing as does digital audio processing over analog audio processing. In addition, digital image processing enables the use of algorithms to perform other tasks, such as three-dimensional visualization and image segmentation.
In digital image processing, segmentation is the partitioning of a digital image into multiple regions in accordance with a given set of criteria. Typically, the goal of segmentation is to locate objects of interest, such as the heart, and separate them from objects of lesser or no interest. For example, segmentation of the heart and its internal structures, such as the four chambers of the heart, is a pre-requisite for three-dimensional visualization of the heart and for performing a quantitative analysis of the function of the heart. This can be very valuable information for the diagnosis and treatment of cardiovascular disease. However, heart segmentation is challenging due a number of factors. One factor is the natural variability in the intensity of the image of the chambers of the heart, which is enhanced by the addition of a contrast agent. Contrast agents are used to selectively highlight anatomical structures, such as blood vessels, and organs, such as the heart and liver. Variations in the injection mechanism may cause these structures to vary in intensity from image to image. In addition, the unpredictability of patient metabolism and the use of new acquisition protocols, such as the saline flush protocol, further complicate the task, making intensity based segmentation tools unreliable.
A need exists for a technique for performing image segmentation that overcomes the problems and difficulties in current imaging systems. In particular, there is a need for an image segmentation technique that does not rely on image intensity consistency across an anatomical feature. The technique provided below may solve one or more of the problems described above.